Qanats are a remarkable type of ancient hydraulic structure for sustainable water distribution in arid environments that use subterranean channels to transport water from highland or mountainous areas. The presence of the qanat system is marked by a line of regularly spaced shafts visible from the surface, which can be used to detect qanats using satellite imagery. Typically, qanats have been documented by field mapping or manual digitisation within a Geographic Information System (GIS) environment. This process is time-consuming due to the numerous shafts within each qanat line. However, several automated methods for detecting qanat structures have been explored, using techniques such as morphological filters, custom convolutional neural networks (CNN) and, more recently, YOLOv5 and Mask R-CNN. These approaches used high-resolution RGB images and CORONA images. However, the use of black and white CORONA in CNNs has been limited in its applicability due to a high rate of false positives.

中文翻译：

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使用 HEXAGON 间谍卫星图像对坎儿井地下配水系统进行基于深度学习的检测


坎儿井是一种非凡的古代水利结构，用于在干旱环境中进行可持续配水，利用地下通道从高原或山区输送水。坎儿井系统的存在是由一排从表面可见的规则间隔的轴来标记的，可用于使用卫星图像来检测坎儿井。通常，坎儿井是通过地理信息系统 （GIS） 环境中的实地映射或手动数字化来记录的。由于每条坎儿井生产线内有许多轴，因此此过程非常耗时。然而，已经探索了几种检测坎儿井结构的自动化方法，使用形态学过滤器、自定义卷积神经网络 （CNN） 以及最近的 YOLOv5 和 Mask R-CNN 等技术。这些方法使用高分辨率 RGB 图像和 CORONA 图像。然而，由于假阳性率高，在 CNN 中使用黑白 CORONA 的适用性受到限制。